Peratham Wiriyathammabhum Peratham

## install-tensorflow.sh
# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

## readme.md

      
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                Peratham
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              Created
              May 21, 2017 23:32
                — forked from vsubhashini/readme.md
            
              
                Sequence to Sequence - Video to Text (S2VT)
              
          
    ##Sequence to Sequence -- Video to Text
Paper : ICCV 2015 PDF
Download Model: S2VT_VGG_RGB_MODEL (333MB)
Project Page
Description


## tf_lstm.py
"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

## deploy.prototxt
name: "GoogleNet"

input: "data"
input_dim: 10
input_dim: 3
input_dim: 224
input_dim: 224

# hierarchy 1
# conv -> relu -> pool -> lrn
	# Note – this is not a bash script (some of the steps require reboot)
	# I named it .sh just so Github does correct syntax highlighting.
	#
	# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
	#
	# The CUDA part is mostly based on this excellent blog post:
	# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

	# Install various packages
	sudo apt-get update
	"""Short and sweet LSTM implementation in Tensorflow.

	Motivation:
	When Tensorflow was released, adding RNNs was a bit of a hack - it required
	building separate graphs for every number of timesteps and was a bit obscure
	to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
	Currently the APIs are decent, but all the tutorials that I am aware of are not
	making the best use of the new APIs.

	Advantages of this implementation:
	name: "GoogleNet"

	input: "data"
	input_dim: 10
	input_dim: 3
	input_dim: 224
	input_dim: 224

	# hierarchy 1
	# conv -> relu -> pool -> lrn